BIOINFORMATICS APPLICATIONS UNDER CONDITION CONTROL: HIGH DIAGNOSTIC VALUE OF DDX47 IN REAL MEDICAL SETTINGS.

ABSTRACT: Sepsis is an organ dysfunction caused by a dysregulated host response to infection and remains an ongoing threat to human health worldwide. Septic shock is the most severe subset of sepsis as characterized by abnormalities in cells, circulation, and metabolism. As a time-dependent condition, early recognition allowing appropriate therapeutic measures to be started in a timely manner becomes the most effective way to improve prognosis. However, because of the lack of a criterion standard, most diagnoses merely rely on medical history, empirical diagnosis, and blood culture results. Gene expression profiles have specific diagnostic value, as they reflect a subjective host response to pathogens. We propose a method, Condition Control based on Real-life Medical Scenarios, to control for factors in realistic medical scenarios. Restricted variables are used as much as possible to identify unique differential genes and progressively test their diagnostic value by relaxing restrictions. In total, three data sets were included in the study; the first two data sets were from the Gene Expression Omnibus database, and the third involved patients who were diagnosed with sepsis or septic shock within 7 days of admission to the intensive care unit at West China Hospital of Sichuan University from 2020 to 2021. DDX47 showed preferable diagnostic value in various scenarios, especially in patients with common infections or sepsis and septic shock. Here we also show that hub genes may regulate immune function and immune cell counts through the interaction of different apoptotic pathways and immune checkpoints based on the high correlation. DDX47 is closely associated with B cells according to single-cell sequencing results.

Mitophagy-related genes could facilitate the development of septic shock during immune infiltration.

Septic shock often occurs following critically low blood pressure in patients with sepsis, and is accompanied by a high death rate. Although mitophagy is associated with infection and immune responses, its role in septic shock remains unknown. This study screened effective mitophagy-related genes (MRGs) for medical practice and depicted immune infiltration situations in patients with septic shock. Gene expression profiles of GSE131761 from the Gene Expression Omnibus database were compiled for differential analysis, weighted gene co-expression network analysis, and immune infiltration analysis, while other GSE series were used as validation datasets. A series of validation methods were used to verify the robustness of hub genes, while a nomogram and prognosis model were established for medical practice. Six genes were screened via combinations of differentially expressed genes, weighted gene co-expression network analysis, and MRGs. From this, 3 hub genes (MAP1LC3B, ULK1, and CDC37) were chosen for subsequent analysis based on different validation methods. Gene set enrichment analysis showed that leukocyte trans-endothelial migration and the p53 signaling pathway were abnormally activated during septic shock. Immune infiltration analysis indicated that the imbalance of neutrophils and CD4 naive T cells was significantly correlated with septic shock progression. A nomogram was generated based on MAP1LC3B, ULK1, and CDC37, as well as age. The stability of our model was confirmed using a calibration plot. Importantly, patients with septic shock with the 3 highly expressed hub genes displayed worse prognosis than did patients without septic shock. MAP1LC3B, ULK1, and CDC37 are considered hub MRGs in the development of septic shock and could represent promising diagnostic and prognostic biomarkers in blood tissue. The validated hub genes and immune infiltration pattern expand our knowledge on MRG functional mechanisms, which provides guidance and direction for the development of septic shock diagnostic and therapeutic markers.

Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.

Objective: Sepsis related injury has gradually become the main cause of death in non-cardiac patients in intensive care units, but the underlying pathological and physiological mechanisms remain unclear. The Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) definition emphasized organ dysfunction caused by infection. Neutrophil extracellular traps (NETs) can cause inflammation and have key roles in sepsis organ failure; however, the role of NETs-related genes in sepsis is unknown. Here, we sought to identify key NETs-related genes associate with sepsis. Methods: Datasets GSE65682 and GSE145227, including data from 770 patients with sepsis and 54 healthy controls, were downloaded from the GEO database and split into training and validation sets. Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. A machine learning approach was applied to identify key genes, which were used to construct functional networks. Key genes associated with diagnosis and survival of sepsis were screened out. Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN). Results: Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms. Of these, CYBB and FCAR were independent predictors of poor survival in patients with sepsis. Administration of SFN significantly alleviated murine lung NETs expression and injury, accompanied by whole blood CYBB mRNA level. Conclusion: CYBB and FCAR may be reliable biomarkers of survival in patients with sepsis, as well as potential targets for sepsis treatment. SFN significantly alleviated NETs-related organs injury, suggesting the therapeutic potential by targeting CYBB in the future.

Toxin exposure and HLA alleles determine serum antibody binding to toxic shock syndrome toxin 1 (TSST-1) of Staphylococcus aureus.

Life-threatening toxic shock syndrome is often caused by the superantigen toxic shock syndrome toxin-1 (TSST-1) produced by Staphylococcus aureus. A well-known risk factor is the lack of neutralizing antibodies. To identify determinants of the anti-TSST-1 antibody response, we examined 976 participants of the German population-based epidemiological Study of Health in Pomerania (SHIP-TREND-0). We measured anti-TSST-1 antibody levels, analyzed the colonization with TSST-1-encoding S. aureus strains, and performed a genome-wide association analysis of genetic risk factors. TSST-1-specific serum IgG levels varied over a range of 4.2 logs and were elevated by a factor of 12.3 upon nasal colonization with TSST-1-encoding S. aureus. Moreover, the anti-TSST-1 antibody levels were strongly associated with HLA class II gene loci. HLA-DRB1*03:01 and HLA-DQB1*02:01 were positively, and HLA-DRB1*01:01 as well as HLA-DQB1*05:01 negatively associated with the anti-TSST-1 antibody levels. Thus, both toxin exposure and HLA alleles affect the human antibody response to TSST-1.

Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes.

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.

Gene Co-Expression Networks Offer New Perspectives on Sepsis Pathophysiology.

Sepsis is among the most common causes of death in intensive care units. Septic shock is a type of circulatory shock that shows signs and symptoms that are similar to non-septic shock. Despite the impact of shock on patients and the economic burden, knowledge of the pathophysiology of septic shock is scarce. In this context, weighted gene co-expression network analysis can help to elucidate the molecular mechanisms of this condition. The gene expression dataset used in this study was downloaded from the Gene Expression Omnibus, which contains 80 patients with septic shock, 33 patients with non-septic shock, and 15 healthy controls. Our novel analysis revealed five gene modules specific for patients with septic shock and three specific gene modules for patients with non-septic shock. Interestingly, genes related to septic shock were mainly involved in the immune system and endothelial cells, while genes related to non-septic shock were primarily associated with endothelial cells. Together, the results revealed the specificity of the genes related to the immune system in septic shock. The novel approach developed here showed its potential to identify critical pathways for the occurrence and progression of these conditions while offering new treatment strategies and effective therapies.

Identification of module genes and functional pathway analysis in septic shock subtypes by integrated bioinformatics analysis.

BACKGROUND: The present study aimed to identify the module genes and key gene functions and biological pathways of septic shock (SS) through integrated bioinformatics analysis. METHODS: In the study, we performed batch correction and principal component analysis on 282 SS samples and 79 normal control samples in three datasets, GSE26440, GSE95233 and GSE57065, to obtain a combined corrected gene expression matrix containing 21,654 transcripts. Patients with SS were then divided into three molecular subtypes according to sample subtyping analysis. RESULTS: By analyzing the demographic characteristics of the different subtypes, we found no statistically significant differences in gender ratio and age composition among the three groups. Then, three subtypes of differentially expressed genes (DEGs) and specific upregulated DEGs (SDEGs) were identified by differential gene expression analysis. We found 7361 DEGs in the type I group, 5594 DEGs in the type II group, and 7159 DEGs in the type III group. There were 1698 SDEGs in the type I group, 2443 in the type II group, and 1831 in the type III group. In addition, we analyzed the correlation between the expression data of 5972 SDEGs in the three subtypes and the gender and age of 227 patients, constructed a weighted gene co-expression network, and identified 11 gene modules, among which the module with the highest correlation with gender ratio was MEgrey. The modules with the highest correlation with age composition were MEgrey60 and MElightyellow. Then, by analyzing the differences in module genes among different subgroups of SS, we obtained the differential expression of 11 module genes in four groups: type I, type II, type III and the control group. Finally, we analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of all module DEGs, and the GO function and KEGG pathway enrichment of different module genes were different. CONCLUSIONS: Our findings aim to identify the specific genes and intrinsic molecular functional pathways of SS subtypes, as well as further explore the genetic and molecular pathophysiological mechanisms of SS.

CD3D and CD247 are the molecular targets of septic shock.

Septic shock is a serious systemic disease with circulatory failure and abnormal cell metabolism caused by sepsis. However, the relationship between CD3D and CD247 and septic shock remains unclear. The septic shock datasets GSE33118 and GSE142255 profiles were generated from the gene expression omnibus databases GPl570, GPl17586. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. The construction and analysis of protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis (GSEA) were performed. Gene expression heat map was drawn. Immune infiltration analysis was performed. Comparative toxicogenomics database (CTD) analysis were performed to find the disease most related to the core gene. Targets can was used to screen miRNAs regulating the hub DEGs. 467 DEGs were identified. According to the gene ontology analysis, they were mainly enriched in the regulation of immune response, cell activation, signaling receptor activity, enzyme binding. Kyoto encyclopedia of genes and genomes analysis showed that they were mainly enriched in the TCR signaling pathway, Fc epsilon RI signaling pathway. GSEA showed that the DEGs were mainly enriched in immune response regulation, cell activation, TCR signaling pathway, Fc epsilon RI signaling pathway. Positive regulation of Fc receptor signaling pathway, PID IL12 2 pathway, immune response was observed in go enrichment items in the enrichment items of metascape. PPI networks got 5 core genes. Gene expression heat map showed that 5 core genes (CD247, Lck, cd3e, cd3d, ITK) were lowly expressed in the sepsis shock samples and highly expressed in the normal samples. CTD analysis showed that 5 core genes (CD247, Lck, cd3e, cd3d, ITK) were found to be associated with hemorrhage and necrosis. Low expression of cd3d, CD247 was observed in septic shock, and the lower the level of cd3d, CD247, the worse the prognosis.

Prognostic Value of HIF-1α-Induced Genes in Sepsis/Septic Shock.

Hypoxia is characterized as one of the main consequences of sepsis, which is recognized as the leading cause of death in intensive care unit (ICU) patients. In this study, we aimed to examine whether the expression levels of genes regulated under hypoxia could be utilized as novel biomarkers for sepsis prognosis in ICU patients. Whole blood expression levels of hypoxia-inducible factor-1α (HIF1A), interferon-stimulated gene 15 (ISG15), hexokinase 2 (HK2), lactate dehydrogenase (LDHA), heme oxygenase-1 (HMOX1), erythropoietin (EPO), and the vascular endothelial growth factor A (VEGFA) were measured on ICU admission in 46 critically ill, initially non-septic patients. The patients were subsequently divided into two groups, based on the development of sepsis and septic shock (n = 25) or lack thereof (n = 21). HMOX1 mRNA expression was increased in patients who developed sepsis/septic shock compared to the non-septic group (p < 0.0001). The ROC curve, multivariate logistic regression, and Kaplan-Meier analysis demonstrated that HMOX1 expression could be utilized for sepsis and septic shock development probability. Overall, our results indicate that HMOX1 mRNA levels have the potential to be a valuable predictive factor for the prognosis of sepsis and septic shock in ICU patients.

Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study.

Background: Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency. Methods: The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in septic shock was determined using receiver operating characteristic (ROC) analysis and verified using quantitative PCR (qPCR) and Western blotting in our hospital patients with septic shock. Results: A total of 975 DEGs in the GSE33118 and GSE26440 databases were obtained, of which 30 DEGs were remarkably upregulated. With the use of Lasso regression and Boruta feature selection algorithm, six hub genes (CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4) with expression differences in septic shock were screened as potential diagnostic markers for septic shock among the significant DEGs and were further validated in the GSE9692 dataset. WGCNA was used to identify the co-expression modules and module-trait correlation. Enrichment analysis showed significant enrichment in the reactive oxygen species pathway, hypoxia, phosphatidylinositol 3-kinases (PI3K)/Protein Kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling, nuclear factor-κß/tumor necrosis factor alpha (NF-κß/TNF-α), and interleukin-6 (IL-6)/Janus Kinase (JAK)/Signal Transducers and Activators of Transcription 3 (STAT3) signaling pathways. The receiver operating characteristic curve (ROC) of these signature genes was 0.938, 0.914, 0.939, 0.956, 0.932, and 0.914, respectively. In the immune cell infiltration analysis, the infiltration of M0 macrophages, activated mast cells, neutrophils, CD8 T cells, and naive B cells was more significant in the septic shock group. In addition, higher expression levels of CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 messenger RNA (mRNA) were observed in peripheral blood mononuclear cells (PBMCs) isolated from septic shock patients than from healthy donors. Higher expression levels of CD177 and MMP8 proteins were also observed in the PBMCs isolated from septic shock patients than from control participants. Conclusions: CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 were identified as hub genes, which were of considerable value in the early diagnosis of septic shock patients. These preliminary findings are of great significance for studying immune cell infiltration in the pathogenesis of septic shock, which should be further validated in clinical studies and basic studies.

Detrimental effects of PCSK9 loss-of-function in the pediatric host response to sepsis are mediated through independent influence on Angiopoietin-1.

BACKGROUND: Sepsis is associated with significant mortality. Yet, there are no efficacious therapies beyond antibiotics. PCSK9 loss-of-function (LOF) and inhibition, through enhanced low-density lipoprotein receptor (LDLR) mediated endotoxin clearance, holds promise as a potential therapeutic approach among adults. In contrast, we have previously demonstrated higher mortality in the juvenile host. Given the potential pleiotropic effects of PCSK9 on the endothelium, beyond canonical effects on serum lipoproteins, both of which may influence sepsis outcomes, we sought to test the influence of PCSK9 LOF genotype on endothelial dysfunction. METHODS: Secondary analyses of a prospective observational cohort of pediatric septic shock. Genetic variants of PCSK9 and LDLR genes, serum PCSK9, and lipoprotein concentrations were determined previously. Endothelial dysfunction markers were measured in day 1 serum. We conducted multivariable linear regression to test the influence of PCSK9 LOF genotype on endothelial markers, adjusted for age, complicated course, and low- and high-density lipoproteins (LDL and HDL). Causal mediation analyses to test impact of select endothelial markers on the association between PCSK9 LOF genotype and mortality. Juvenile Pcsk9 null and wildtype mice were subject to cecal slurry sepsis and endothelial markers were quantified. RESULTS: A total of 474 patients were included. PCSK9 LOF was associated with several markers of endothelial dysfunction, with strengthening of associations after exclusion of those homozygous for the rs688 LDLR variant that renders it insensitive to PCSK9. Serum PCSK9 was not correlated with endothelial dysfunction. PCSK9 LOF influenced concentrations of Angiopoietin-1 (Angpt-1) upon adjusting for potential confounders including lipoprotein concentrations, with false discovery adjusted p value of 0.042 and 0.013 for models that included LDL and HDL, respectively. Causal mediation analysis demonstrated that the effect of PCSK9 LOF on mortality was mediated by Angpt-1 (p = 0.0008). Murine data corroborated these results with lower Angpt-1 and higher soluble thrombomodulin among knockout mice with sepsis relative to the wildtype. CONCLUSIONS: We present genetic and biomarker association data that suggest a potential direct role of the PCSK9-LDLR pathway on Angpt-1 in the developing host with septic shock and warrant external validation. Further, mechanistic studies on the role of PCSK9-LDLR pathway on vascular homeostasis may lead to the development of pediatric-specific sepsis therapies.

Pharmacogenetics in critical care: association between CYP3A5 rs776746 A/G genotype and acetaminophen response in sepsis and septic shock.

BACKGROUND: Pharmacogenetics could represent a further resource to understand the interindividual heterogeneity of response of the host to sepsis and to provide a personalized approach to the critical care patient. METHODS: Secondary analysis of data from the prospective observational study NCT02750163, in 50 adult septic and septic shock patients treated with Acetaminophen (ACT) for pyrexia. We investigated the presence of two polymorphisms, located respectively in the genes UGT1A1 and CYP3A5, that encode for proteins related to the hepatic metabolism of ACT. The main dependent variables explored were plasmatic concentration of ACT, body temperature and hepatic parameters. RESULTS: 8% of the patients carried CYP3A5 rs776746 A/G genotypes and showed significantly higher plasma levels of ACT than GG wild type patients, and than patients with UGT1A1 rs8330 C/G genotypes. CONCLUSIONS: Identifying specific genotypes of response to ACT may be helpful to guide a more personalized titration of therapy in sepsis and septic shock. CYP3A5 might be a good biomarker for ACT metabolism; however further studies are needed to confirm this result. TRIAL REGISTRATION: NCT02750163.

Long Noncoding RNA U90926 Is Induced in Activated Macrophages, Is Protective in Endotoxic Shock, and Encodes a Novel Secreted Protein.

Thousands of long noncoding RNAs are encoded in mammalian genomes, yet most remain uncharacterized. In this study, we functionally characterized a mouse long noncoding RNA named U90926. Analysis of U90926 RNA levels revealed minimal expression across multiple tissues at steady state. However, the expression of this gene was highly induced in macrophages and dendritic cells by TLR activation, in a p38 MAPK- and MyD88-dependent manner. To study the function of U90926, we generated U90926-deficient (U9-KO) mice. Surprisingly, we found minimal effects of U90926 deficiency in cultured macrophages. Given the lack of macrophage-intrinsic effect, we investigated the subcellular localization of U90926 transcript and its protein-coding potential. We found that U90926 RNA localizes to the cytosol, associates with ribosomes, and contains an open reading frame that encodes a novel glycosylated protein (termed U9-ORF), which is secreted from the cell. An in vivo model of endotoxic shock revealed that, in comparison with wild type mice, U9-KO mice exhibited increased sickness responses and mortality. Mechanistically, serum levels of IL-6 were elevated in U9-KO mice, and IL-6 neutralization improved endotoxemia outcomes in U9-KO mice. Taken together, these results suggest that U90926 expression is protective during endotoxic shock, potentially mediated by the paracrine and/or endocrine actions of the novel U9-ORF protein secreted by activated myeloid cells.

Alterations in leukocyte DNA methylome are associated to immunosuppression in severe clinical phenotypes of septic patients.

Introduction: Sepsis patients experience a complex interplay of host pro- and anti-inflammatory processes which compromise the clinical outcome. Despite considering the latest clinical and scientific research, our comprehension of the immunosuppressive events in septic episodes remains incomplete. Additionally, a lack of data exists regarding the role of epigenetics in modulating immunosuppression, subsequently impacting patient survival. Methods: To advance the current understanding of the mechanisms underlying immunosuppression, in this study we explored the dynamics of DNA methylation using the Infinium Methylation EPIC v1.0 BeadChip Kit in leukocytes from patients suffering from sepsis, septic shock, and critically ill patients as controls, within the first 24 h after admission in the Intensive Care Unit of a tertiary hospital. Results and discussion: Employing two distinct analysis approaches (DMRcate and mCSEA) in comparing septic shock and critically ill patients, we identified 1,256 differentially methylated regions (DMRs) intricately linked to critical immune system pathways. The examination of the top 100 differentially methylated positions (DMPs) between septic shock and critically ill patients facilitated a clear demarcation among the three patient groups. Notably, the top 6,657 DMPs exhibited associations with organ dysfunction and lactate levels. Among the individual genes displaying significant differential methylation, IL10, TREM1, IL1B, and TNFAIP8 emerged with the most pronounced methylation alterations across the diverse patient groups when subjected to DNA bisulfite pyrosequencing analysis. These findings underscore the dynamic nature of DNA methylation profiles, highlighting the most pronounced alterations in patients with septic shock, and revealing their close association with the disease.

Analysis of signature genes and association with immune cells infiltration in pediatric septic shock.

Background: Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock. Methods: We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT. Results: A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration. Conclusion: UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration.

Significant difference of differential expression pyroptosis-related genes and their correlations with infiltrated immune cells in sepsis.

Background: Sepsis is regarded as a life-threatening organ dysfunction syndrome that responds to infection. Pyroptosis, a unique form of programmed cell death, is characterized by inflammatory cytokine secretion. Recently, an increasing number of studies have investigated the relationship between sepsis and pyroptosis. Appropriate pyroptosis can help to control infection during sepsis, but an immoderate one may cause immune disorders. The present study aimed to identify pyroptosis-related gene biomarkers and their relationship with the immune microenvironment using the genome-wide technique. Methods: The training dataset GSE154918 and the validation dataset GSE185263 were downloaded for bioinformatics analysis. Differentially expressed pyroptosis-related genes (DEPRGs) were identified between sepsis (including septic shock) and healthy samples. Gene Set Enrichment Analysis (GSEA) was performed to explore gene function. CIBERSORT tools were applied to quantify infiltrating immune cells, and the correlation between differentially infiltrating immune cells and DEPRG expression was investigated. Furthermore, based on multivariable Cox regression, the study also utilized a random forest (RF) model to screen biomarkers. Results: In total, 12 DEPRGs were identified. The expression level of PLCG1 was continuously significantly decreased, while the expression level of NLRC4 was elevated from control to sepsis and then to septic shock. GSEA found that one DEPRG (PLCG1) was involved in the T-cell receptor signaling pathway and that many T cell-related immunologic signature gene sets were enriched. The proportions of plasma cells, T cells CD4 memory activated, and some innate cells in the sepsis group were significantly higher than those in the healthy group, while the proportions of T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), and NK cells were lower. Additionally, CASP4 was positively correlated with Neutrophils and negatively correlated with T cells CD4 memory resting and Tregs. Lastly, two biomarkers (CASP4 and PLCG1) were identified, and a nomogram model was constructed for diagnosis with area under the curve (AUC) values of 0.998. Conclusion: This study identified two potential pyroptosis-related diagnostic genes, CASP4 and PLCG1, and explored the correlation between DEPRGs and the immune microenvironment. Also, our study indicated that some DEPRGs were satisfactorily correlated with several representative immune cells that can regulate pyroptosis.

Multiple datasets to explore the molecular mechanism of sepsis.

BACKGROUND: This study aimed to identify potential biomarkers, by means of bioinformatics, affecting the occurrence and development of septic shock. METHODS: Download GSE131761 septic shock data set from NCBI geo database, including 33 control samples and 81 septic shock samples. GSE131761 and sequencing data were used to identify and analyze differentially expressed genes in septic shock patients and normal subjects. In addition, with sequencing data as training set and GSE131761 as validation set, a diagnostic model was established by lasso regression to identify key genes. ROC curve verified the stability of the model. Finally, immune infiltration analysis, enrichment analysis, transcriptional regulation analysis and correlation analysis of key genes were carried out to understand the potential molecular mechanism of key genes affecting septic shock. RESULTS: A total of 292 differential genes were screened out from the self-test data, 294 differential genes were screened out by GSE131761, Lasso regression was performed on the intersection genes of the two, a diagnostic model was constructed, and 5 genes were identified as biomarkers of septic shock. These 5 genes were SIGLEC10, VSTM1, GYPB, OPTN, and GIMAP7. The five key genes were strongly correlated with immune cells, and the ROC results showed that the five genes had good predictive performance on the occurrence and development of diseases. In addition, the key genes were strongly correlated with immune regulatory genes. CONCLUSION: In this study, a series of algorithms were used to identify five key genes that are associated with septic shock, which may become potential candidate targets for septic shock diagnosis and treatment. TRIAL REGISTRATION: Approval number:2019XE0149-1.

Lymphocyte DNA damage in sepsis and septic-shock intensive-care patients: Damage is greater in non-intubated patients.

Sepsis is an excessive host response to infection; septic shock is a more severe clinical condition. We studied 43 sepsis patients, 32 septic-shock patients, and a group of healthy controls. The patients' Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) 2 score were much higher in the septic-shock group than in the sepsis group. We used the comet assay to measure lymphocyte DNA damage; the damage scores were significantly higher in both the sepsis and the septic-shock groups compared to the healthy controls. There was no statistically significant difference between the sepsis and septic-shock groups. We also compared DNA damage levels of intubated vs. non-intubated patients. DNA damage was significantly higher in non-intubated patients compared to intubated patients, for both the sepsis and the septic-shock groups. Early intubation may be beneficial in non-intubated patients who have high levels of DNA damage.

Natural mutation in the regulatory gene (srrG) influences virulence-associated genes and enhances invasiveness in Streptococcus dysgalactiae subsp. equisimilis strains isolated from cases of streptococcal toxic shock syndrome.

BACKGROUND: Streptococcus dysgalactiae subspecies equisimilis (SDSE) has emerged as an important cause of severe invasive infections including streptococcal toxic shock syndrome (STSS). The present study aimed to identify genes involved in differences in invasiveness between STSS and non-invasive SDSE isolates. METHODS: STSS and non-invasive SDSE isolates were analysed to identify csrS/csrR mutations, followed by a comparative analysis of genomic sequences to identify mutations in other genes. Mutant strains were generated to examine changes in gene expression profiles and altered pathogenicity in mice. FINDINGS: Of the 79 STSS-SDSE clinical isolates, 15 (19.0%) harboured csrS/csrR mutations, while none were found in the non-invasive SDSE isolates. We identified a small RNA (sRNA) that comprised three direct repeats along with an inverted repeat and was transcribed in the same direction as the sagA gene. The sRNA was referred to as srrG (streptolysin S regulatory RNA in GGS). srrG mutations were identified in the STSS-SDSE strains and were found to be associated with elevated expression of the streptolysin S (SLS) gene cluster and enhanced pathogenicity in mice. INTERPRETATION: The csrS/csrR and srrG mutations that increased virulence gene expression in STSS-SDSE isolates were identified, and strains carrying these mutations caused increased lethality in mice. A significantly higher frequency of mutations was observed in STSS-SDSE isolates, thereby highlighting their importance in STSS. FUNDING: Japan Agency for Medical Research and Development, the Japan Society for the Promotion of Science (JSPS), and the Ministry of Health, Labor, and Welfare of Japan.

Circular RNAs in the pathogenesis of sepsis and their clinical implications: A narrative review.

INTRODUCTION: Sepsis is defined as a life-threatening complication that occurs when the body responds to an infection attacking the host. Sepsis rapidly progresses and patients deteriorate and develop septic shock, with multiple organ failure, if not promptly treated. Currently no effective therapy is available for sepsis; therefore, early diagnosis is crucial to decrease the high mortality rate. Genome-wide expression analyses of patients in critical conditions have confirmed that the expression levels of the majority of genes are changed, suggesting that the molecular basis of sepsis is at the gene level. This review aims to elucidate the role of circular (circ) RNAs in the pathogenesis of sepsis and sepsis-induced organ damage. In addition, the feasibility of using circRNAs as novel diagnostic biomarkers for sepsis is also discussed, as well as circRNA-based therapy. METHOD: This narrative review is based on a literature search using Medline database. Search terms used were "circular RNAs and sepsis", "circRNAs and sepsis", "non-coding RNAs and sepsis", "ncRNAs and sepsis", "circRNAs and septic pathogenesis", "circRNAs and septic model", "circRNAs and septic shock" and "circRNAs, biomarker, and sepsis". RESULTS: Numerous studies indicate that circRNAs might exert pivotal roles in regulating the immune system of the host against various pathogens, such as bacteria and viruses. Dysregulation of circRNA expression levels has been confirmed as an early event in sepsis and associated with the inflammatory response, immunosuppression and coagulation dysfunction. This impairment in regulation eventually leads to multiple organ dysfunctions, including of the kidneys, lungs and heart. CONCLUSION: By investigating the regulation of circRNAs in sepsis, new molecular targets for the diagnosis and intervention of sepsis can be identified. Such an understanding will be important for the development of therapeutic drugs.